Pancreatic cancer tumor analysis in CT images using patch-based multi-resolution convolutional neural network. (July 2021) - Asadpour, Vahid; Parker, Rex A.; Mayock, Patrick R.; Sampson, Samuel E.; Chen, Wansu; Wu, Bechien
Highlights: An artificial intelligence method for extraction of the shape of the tumor and pancreas is proposed in this paper. The proposed structure is composed of elastic atlas, convolutional neural network, edge detection and rendering stages. Cross-sectional Images are assessed, measured and delineated by experienced body radiologists as the gold standard. Abstract: In this paper we proposed a cascaded structure for extraction of the volumetric shape of the pancreas and tumor in adenocarcinoma patients. This process is a combination of an elastic atlas which is capable of fitting on 3D volumetric shape extracted from CT slices, a convolutional neural network with three forward paths to label the patches of images with coarse to fine resolutions using a multi-resolution structure, a region growing edge detection and finally a wavelet based multi-resolution rendering to extract the volumetric shape of the pancreas and tumor from planar CT slices. The atlas organs were weighted by geometrical parameters which were adjusted at global and organ levels. The classification of image patches was performed using a multiresolution convolutional neural network. The final segmentation of the pancreas and tumor was obtained by applying an edge detection method. A multiresolution wavelet is used for extraction of the volumetric shapes from segmented images. The subjects were 53–86 years of age with a mean and SD of 66 ± 10.24 years. K-fold cross validation is used for all the experiments. The results were compared with those of previously reported methods including Residual Network. The performance of the proposed algorithm was evaluated by Dice Similarity Coefficient (DSC), Jaccard index (JI), precision and recall yielded 89.67, 80.12, 91.37 and 93.63 for pancreas and 81.42, 68.66, 84.97 and 88.24 for tumor, respectively. The results of the proposed fully cascaded method overperformed all the other methods being compared.